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Computational chemistry is the leading application to demonstrate theadvantage of quantum computing in the near term. However, large-scalesimulation of chemical systems on quantum computers is currently hindered dueto a mismatch between the computational resource needs of the program and thoseavailable in today's technology. In this paper we argue that significant newoptimizations can be discovered by co-designing the application, compiler, andhardware. We show that multiple optimization objectives can be coordinatedthrough the key abstraction layer of Pauli strings, which are the basicbuilding blocks of computational chemistry programs. In particular, we leveragePauli strings to identify critical program components that can be used tocompress program size with minimal loss of accuracy. We also leverage thestructure of Pauli string simulation circuits to tailor a novel hardwarearchitecture and compiler, leading to significant execution overhead reductionby up to 99%. While exploiting the high-level domain knowledge revealssignificant optimization opportunities, our hardware/software framework is nottied to a particular program instance and can accommodate the full family ofcomputational chemistry problems with such structure. We believe the co-designlessons of this study can be extended to other domains and hardwaretechnologies to hasten the onset of quantum advantage.

Software-Hardware Co-Optimization for Computational Chemistry on Superconducting Quantum Processors